Image recognition technologies are an important field of artificial intelligence. Image recognition refers to a technology that uses a computer to process and analyze an image, to recognize different targets and objects. In recent years, with the popularity of social networks, there are increasing demands for real-time image data analysis on a mobile device. However, because a relatively large quantity of system resources are consumed for implementation of image data analysis, a limited battery lifespan of a mobile device restricts application of image data analysis to mobile devices.
To reduce a system power consumption during a process of image data analysis, an image data processing method in the prior art reduces the system power consumption in a manner of lowering a write current that is used to write image data into a static random-access memory (SRAM). However, an error rate of data stored in the SRAM increases as the write current is lowered. To recover from an error, a manner such as convex optimization processing is further required to recover the stored image data, so that image recognition can be performed based on the recovered image data. In this manner, the system power consumption is reduced to some extent when data is written, but CPU computing complexity is high during a process of image recovery, which wastes a considerable quantity of system resources. In addition, to protect the data stored in the SRAM, the SRAM needs to stay in a power-on state. Therefore, the SRAM also has a static power consumption. In the foregoing image data processing manner, the static power consumption that is required for the SRAM to protect the data still cannot be eliminated. As a result, generally, when the existing image data processing manner is used to process image data, a system power consumption is still relatively high.